86 results about "Process automation system" patented technology

A method of operating a vehicle in a transportation system includes: receiving data collected for each of a set of users based on interactions of the set of users with a user interface of the vehicle including at least one of a vehicle data system, a vision system, and a connected system; performing robotic process automation on the interactions; learning changes in driving style using an artificial intelligence system based on the robotic process automation; and undertaking an action within the vehicle on behalf of the user based on the learned changes in driving style.

A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

An information technology system generally including a cloud-based management platform with a micro-services architecture having a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.

The invention discloses a mold injection molding full-process automation system based on layered cooperative control, which comprises a central control layer, a module scheduling layer and an equipment execution layer, and clock synchronization among the layers is realized through an industrial Ethernet. The central control layer comprises a dynamic scheduling server and a digital twinborn verification module, the module scheduling layer comprises a mold intelligent scheduling module, an injection molding process control module and a product quality closed loop module, and the equipment execution layer comprises a manipulator cooperation unit and a sprue material processing unit. By constructing a three-layer cooperative control architecture of a central control layer, a module scheduling layer and an equipment execution layer and combining a modular hardware carrier and a cross-layer data closed loop, common industrial problems such as cooperative level splitting, mold control marginalization and abnormal response delay are systematically solved; and finally, the comprehensive benefits that the die changing efficiency is remarkably improved, the product reject ratio is greatly reduced, and the energy consumption is obviously reduced are achieved.

An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I / O control and managing multiple computers as an intelligent process automation system.

An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I / O control and managing multiple computers as an intelligent process automation system.

In variants, a method for robotic process automation (RPA) includes: receiving an automation request; optionally determining an application representation of an application associated with the automation request; generating a set of tasks based on the automation request; optionally classifying each task in the set of tasks; generating a set of instructions for each task in the set of tasks; optionally validating the set of instructions; and optionally executing the set of instructions cooperatively forming an RPA bot.

A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic strategy configuration-based cancel-after-verification process automation system, which comprises the steps of obtaining cancel-after-verification interaction information, performing scene feature extraction and strategy element deconstruction, and forming a strategy element knowledge base; analyzing the voucher identification code, performing multi-dimensional voucher attribute deconstruction and type portrait description, and generating a voucher intelligent portrait; performing rule matching degree evaluation and strategy source tracing, and locking an applicable strategy rule set; performing strategy conflict harmonic analysis and rule priority ranking to generate a cancel-after-verification execution instruction chain; performing flow wave crest prediction and resource scheduling arrangement to form a load balancing strategy table; performing intelligent channel shunting scheduling, and establishing a cancel-after-verification process execution network; according to the invention, the intelligent automation of the cancel-after-verification process is realized, the system processing efficiency is improved, and the method can adapt to complex and changeable business scene requirements.

Implementations relate to storing, in alarms database for a process automation system, an association of an alarm configuration file to a function block identifier of a function block that corresponds to the alarm configuration file. Some implementations relate to a node transmitting, via a process automation network, an alarm configuration request that includes the function block identifier and receiving, in response to the request and via the network, the alarm configuration file. The node can include the function block identifier in the alarm configuration request based on the corresponding function block being executed by the node and / or being utilized in alarm monitoring by the node. Some implementations additionally or alternatively relate to receiving the alarm configuration request, and identifying the alarm configuration file based on it being stored, in the alarms database, in association with the function block identifier of the alarm configuration request.

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, energy storage, energy delivery, and / or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources; and a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, energy storage, energy delivery, and / or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources; and a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

This application provides a method, apparatus, storage medium, and electronic device for reviewing merchant annual inspection work orders, relating to the field of work order review technology. It enables automated review of merchant annual inspection work orders, shortening the review cycle, improving review efficiency, enhancing the standardization and objectivity of the review process, and further improving the accuracy of the review results. The method includes: automatically logging into a merchant annual inspection work order system through a robotic process automation system to obtain a target annual inspection work order for a target merchant and a corresponding image for review of the target merchant. The target annual inspection work order includes the annual inspection items to be reviewed and the registration information of the target merchant. The image for review is processed using a visual large-scale model based on preset information processing rules to obtain structured information corresponding to the annual inspection items to be reviewed. The structured information and registration information are compared according to preset review rules for the annual inspection items to be reviewed to generate the review result for the target merchant.

Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault / failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.

The invention relates to a method for diagnosing a situation in a process automation system (1). A field device (2) is installed in the system (1) and data about the system (1) are provided. Based on the data and on the basis of a prototype associated with the field device (2), a system model is generated using artificial intelligence. On the basis of a measured value generated by the field device (2), a statement about a situation in the system (1) is generated using the system model.

A document process automation system includes a user interface for uploading a document and invoking an artificial intelligence (AI) content assistant. When invoked, a chat window for the Al content assistant is displayed. The Al content assistant leverages a chat service to enable querying a status of the document through the chat window. The system further includes a document enrichment module, an inbound application programming interface (API), and a document API. The document enrichment module determines a category of the document and attaches attributes associated with the category to the document. The inbound API reads the document and creates a copy of the document for a document management system. The document API reads status updates to the copy of the document from a document queue and updates the attributes associated with the document. The user interface is configured for presenting the attributes thus updated as properties of the document.

An industrial process automation system includes a field device level including a plurality of autonomous field devices, and an edge device. Each of the plurality of field devices includes a signal generating module generating a measurement signal, a diagnostic module generating diagnostic data based on the measurement signal, an anomaly detection algorithm that identifies an anomaly based on the diagnostic data, and a communication module transmitting the anomaly to the edge device and then to a cloud / server.

Implementations relate to receiving, from a distributed control node (DCN) that includes a corresponding active alarm engine, an alarm configuration transmission that includes a network addressable DCN identifier of the DCN and also includes a direct or indirect indication that the DCN is implementing alarm monitoring that is based on function block(s) of a process automation system. Implementations further relate to, in response to receiving the alarm configuration transmission and determining that no persistent connection-oriented communication session is established between an alarm viewer and the DCN indicated by the addressable DCN identifier of the alarm configuration transmission, causing the alarm viewer to establish a persistent connection-oriented communication session with the DCN using the addressable DCN identifier.

A method for inputting product parameters, related devices, and storage media provided by this application are applied to a robotic process automation system. The method includes: obtaining a parameter table of a newly added product; determining the target product type to which the newly added product belongs; opening a product addition page corresponding to the target product type; on the basic information tab of the product addition page, inputting the identification information of the newly added product based on the product addition content in the parameter table; generating a maintenance tab for the target parameter content of the newly added product on the product addition page according to the identification information of the newly added product; respectively inputting the target parameter content in the parameter table into each field under the corresponding maintenance tab; and saving the basic information tab and each maintenance tab. Thus, through the robotic process automation system, the problems of too low efficiency of manually inputting product parameters and being prone to errors during the input process are solved, and the input efficiency of product parameters is effectively improved.

Provided are a robotic process automation system and method across operating systems. The system includes an automation process development platform, a control center, and a robot. The automation process development platform is configured to design a corresponding automation business process according to a business scenario requirement, translate the automation business process into a corresponding process code, perform cross-platform encapsulation on the process code, and package the encapsulated cross-platform process code and send the encapsulated cross-platform process code to the control center. The control center is configured to schedule the robot deployed in different operating systems to execute the automation business process corresponding to the cross-platform process code. The robot is configured to download the cross-platform process code to a local operating system, call a process parser of a corresponding category to perform process parsing on the cross-platform process code, and execute the parsed automation business process.

Provided is a system that includes at least one processor programmed or configured to provision a client device for access to an online source of information, transmit a private encryption key of a public / private encryption key pair to a software agent of the client device, receive a first hash value from the software agent, wherein the first hash value is generated using the private encryption key, receive a second hash value from the software agent, determine whether to allow access to the online source of information by the software agent based on the first hash value and the second hash value received from the software agent, process a request to access the online source of information involving the software agent, and store a data record associated with a data transaction involving the online source of information in a data structure. Methods and computer program products are also provided.

A system and method for data aggregation and process automation is disclosed. The method includes receiving a first data object from a first integration point through a first smart adapter, identifying an appropriate rules library from a plurality of rules libraries using a rules engine, the appropriate rules library being identified using the first data object, and applying the appropriate rules library through the rules engine. The rules are applied by instructing a transformation module to transform the first data object into a transformed data object, instructing a validation module to validate at least one of the first data object and the transformed data object, and instructing an aggregation module to perform a statistical analysis on one of the first data object and the transformed data object. Finally, the method includes sending the transformed data object to a second integration point associated with a second smart adapter.

Systems and methods for implementing robotic process automation (RPA) in the cloud are provided. An instruction for managing an RPA robot is received at an orchestrator in a cloud computing environment from a user in a local computing environment. In response to receiving the instruction, the instruction for managing the RPA robot is effectuated.

Systems and methods of robotic process automation including: receiving a multi-threaded task request from a messaging application; automatically dividing the multi-threaded task request into a plurality of subtasks based on metadata associated with the multi-threaded task requests; generating a queue comprising one or more subtasks from the plurality of subtasks; identifying an application programming interface associated with the one or more subtasks from the queue; routing the one or more subtasks to the application programming interface; receiving a response from the application programming interface associated with executing the one or more subtasks; and providing the response to the messaging application.

Systems, computer program products, and methods are described herein for decentralized edge computing enablement in robotic process automation. The present invention is configured to receive an indication that a hosted virtual desktop (HVD) has received a first set of instructions for execution from a controller hosted virtual desktop (CHVD); electronically receive, from the HVD, an indication that the first set of instructions have been executed by the HVD; predict, using the edge computing enablement engine, a second task to be executed by the HVD; determine, using the quantum database search algorithm, a location of the second task in the knowledge repository; retrieve a second set of instructions associated with the second task from the location of the second task in the knowledge repository; and receive, from the HVD, an indication that the second set of instructions have been executed by the HVD.

Systems and methods for configuring an RPA (robotic process automation) platform to perform a candidate process automation are provided. Discovery data relating to a candidate process automation is generated. RPA platform design components for configuring an RPA platform to perform the candidate process automation are generated based on the discovery data. The RPA platform is configured based on the RPA platform design components.

The invention relates to a process valve device (2) for process automation, comprising a valve element (6) for influencing a process fluid, in particular a liquid to be dispensed, preferably metered, wherein the process valve device (2) has a pneumatic drive section (7) for driving the valve element (6), wherein the drive section (7) comprises a piston (8) coupled to the valve element (6) and at least one pressure chamber (9) acting on the piston (8), wherein the process valve device (2) further comprises a control device (19) which is configured to perform position control for positioning the piston (8) and / or valve element (6) along a positioning path by pneumatically actuating the at least one pressure chamber (9), in order to thereby influence the process fluid with the valve element (6).

The invention provides a risk screening method which can be applied to the technical field of artificial intelligence or big data. The method comprises the steps of obtaining message data of a customer in response to a transaction request of the customer; performing hit processing on the message data based on a predefined target list to obtain a target message and corresponding hit information, and uploading the target message and the hit information to a pre-trained natural language processing model through a process automation system; judging the hit information by using a natural language processing model to obtain a judgment result of the target message, and transmitting the judgment result back to the process automation system; and marking the transaction processing result of the customer through the process automation system based on the judgment result. The invention further provides a risk screening device and equipment, a storage medium and a program product.

In variants, a method for robotic process automation (RPA) includes: receiving an automation request; optionally determining an application representation of an application associated with the automation request; generating a set of tasks based on the automation request; optionally classifying each task in the set of tasks; generating a set of instructions for each task in the set of tasks; optionally validating the set of instructions; and optionally executing the set of instructions cooperatively forming an RPA bot.